Delta Latent Dirichlet Allocation

Overview

This software implements the DeltaLDA model [1] for discrete count
data. DeltaLDA is a modification of the Latent Dirichlet Allocation
(LDA) model [2] which uses two different topic mixing weight priors to
jointly model two corpora with a shared set of topics. The inference
method is Collapsed Gibbs sampling [3]. This code can also be used to
do "standard" LDA, similar to [3].

The code implements DeltaLDA as a Python C extension module, combining
the speed of Python with the flexibility and ease-of-use of raw C ;)